Dosing of incretin pathway drugs

The present disclosure relates to systems and methods for determining a dosage of an incretin pathway drug, such as semaglutide, for administering to a patient.

The natural GLP1 (Glucagon-like peptide-1) hormone suppresses appetite and increases satiety. GLP1 slows gastric emptying and may additionally contribute to satiation via increased thermogenesis.

GLP1 agonists (Glucagon-like peptide-1 receptor agonists) are a synthetic analogue derivative of the natural GLP1 hormone which have been modified to increase stability in the human body. GLP1 agonists, such as Semaglutide, may be provided with drug label instructions having a substantially fixed dosing regimen (only providing flexibility if side effects are experienced) which advises use in combination with multidisciplinary dietary advice, including from a dietitian. These nondrug therapies are delivered as a standalone treatment independent of the drug dosage regimen (e.g. face to face therapy, written dietary instructions or a separate diet app).

According to a first aspect of the present disclosure there is provided a computer implemented method for determining a dosage of an incretin pathway drug for administering to a patient for the treatment of obesity, the method comprising:

The incretin pathway drug may comprise semaglutide.

The incretin pathway drug may comprise a GIP agonist or a dual GLP1/GIP agonist.

The incretin pathway drug may comprise tirzepatide.

The incretin pathway drug may comprise a GLP-1 receptor agonist. The incretin pathway drug may comprise one of: albiglutide, dulaglutide, exenatide, liraglutide, efpeglenatide (also called langlenatide) or lixisenatide. The incretin pathway drug may comprise one of: beinaglutide or ecnoglutide. The incretin pathway drug may comprise an oxyntomodulin analogue such as DA-1726.

The incretin pathway drug may comprise a GLP-1 and glucagon receptor co-agonist. The incretin pathway drug may comprise one of: bamadutide, cotadutide, pegapamodutide, pemvidutide, or survodutide. The incretin pathway drug may comprise a GLP-1/GCCR/GIP triple agonist such as retatrutide.

The incretin pathway drug may include: maridebart cafraglutide (Maritide), cagrilintide/semaglutide (cagrisema), pemvidutide, mazdutide or efinopegdutide. The incretin pathway drug may comprise a GLP-1, GCCR and/or GIP drug in development including: Orforglipron, CT-388, CT-868, GX-G6, GMA 105, HM11260C, Danuglipron, GSBR-1290, Pegapamodutide, VK-2735, Dapiglutide, AMG-786, ECC5004, YH 25724, CT-996, DD-01, UBT251, HM15136, HDM1002, NNC-9204-1706, PB-119, SAR425899 [68Ga] Ga-DO3A-VS-Cys40-Tuna-2, XW 014, XW-004, TAK-094, GMA-106, Utreglutide, TERN 601 or DR-10624.

The dosage calculator may be derived from a pharmacokinetic, PK, model.

The PK model may comprise a personalised energy intake-total daily energy expenditure model.

The method may comprise processing the patient data with the dosage calculator to determine an initial dosage for administering to the patient and indicating the initial dosage.

Processing the patient data with the dosage calculator to determine an initial dosage may comprise:

The target end point may comprise one or more of: a target weight loss; and a target side effect level.

The method may comprise:

The patient weight data may include an initial weight and a target weight. The method may comprise processing the initial weight and the target weight with the dosage calculator to determine the initial dosage.

Processing the initial weight and the target weight with the dosage calculator to determine the dosage may comprise:

The target drug concentration may comprise: a target drug plasma level, a target whole blood drug level or a target drug serum level.

The incretin pathway drug may comprise semaglutide and the method may comprise:

The patient data may comprise one or more of: a patient age; a patient ethnicity; a patient gender; a patient diabetes history; a kidney function metric; a pharmacogenomic profile; a treatment purpose; an obesity type; a dosage route and a patient medication list.

The patient data further may comprise one or more of: a patient age; a patient ethnicity; a patient gender; a patient diabetes history; a kidney function metric; a treatment purpose; a dosage route; a pharmacogenomic profile and a patient medication list, and wherein the method the method may comprise determining the initial dosage based on the target drug concentration and the patient data.

The dosage route may comprise an oral dosage and the patient data may further comprise fasting time data and water intake data.

The dosage route may comprise a subcutaneous dosage and the patient data may further comprise an injection site.

The patient data may further comprise a patient side effect tolerance and the method may comprise processing the patient side effect tolerance with the dosage calculator to determine an initial dosage.

Processing the patient side effect tolerance with the dosage calculator may comprise:

The target drug concentration may comprise: a target drug plasma level, a target whole blood drug level and a target drug serum level.

The method may comprise:

The dosage calculator may comprise a side-effect calculator derived from a side effect model. The side effect model may comprise a discrete-time Markov side effect model or an Emax exposure response side effect model.

Processing the patient side effect tolerance and the patient data with the dosage calculator may comprise processing the patient side effect tolerance and the patient data with the side effect calculator. The side effect calculator may comprise a machine learning model. The machine learning model may have been trained using simulated population data generated by the discrete-time Markov side effect model or the Emax exposure response side effect model. The simulated population data may be generated by processing simulated patient data with the discrete-time Markov model or the Emax exposure side effect model. The simulated population data may be generated by processing the simulated patient data with the discrete-time Markov model at a plurality of times, and defining the simulated population data as summary metrics. The summary metrics may comprise one or more of: the mean number of adverse event occasions, the mean duration of each adverse event occasion, the mean time spent experiencing each adverse event, the mean time spent experiencing an adverse event of a given severity, the mean proportion of the entire treatment period spent experiencing each adverse event, and the mean proportion of the entire treatment period spent experiencing an adverse event of a given severity. The side effect tolerance may comprise one or more of: a gastro-intestinal side effect tolerance, a nausea side effect tolerance, a vomiting side effect tolerance; and a diarrhoea side effect tolerance.

Processing the patient side effect tolerance with the dosage calculator may comprise:

The dosage may comprise an initial dosage and the method may comprise:

The method may further comprise:

Processing the updated patient data with the dosage calculator to determine an updated dosage may comprise one or more of:

The dosage may comprise an initial dosage and the method may comprise determining a weight loss trajectory based on the initial dosage. The lower weight loss threshold and upper weight loss threshold may correspond to a prediction range of the weight loss trajectory.

The updated patient data may include patient satiety data. Processing the updated patient data with the dosage calculator to determine an updated dosage may comprise one or more of:

The patient satiety data may include a patient satiety score or a patient hunger score.

Indicating the updated dosage may comprise indicating the updated dosage if an elapsed time since a previous dose adjustment exceeds a drug effect time threshold.

The updated patient data may include reported side effect data. Processing the updated patient data with the dosage calculator to determine an updated dosage may comprise reducing the dosage if the reported side effect data satisfies one or more side effect severity thresholds.

The updated patient data may include reported side effect data and processing the updated patient data with the dosage calculator to determine an updated dosage may comprise reducing the dosage if the reported side effect data satisfies one or more side effect severity thresholds unless the reported side effect data indicates a patient willingness to proceed.

The updated patient data may include a patient metabolism metric from a physiological test result. The method may comprise updating the dosage calculator based on the patient metabolism metric.

The dosage calculator may be derived from a PK model comprising a time-based differential equation model for modelling a time dependence of a plasma concentration of the incretin pathway drug as a function of the patient data.

The dosage calculator may be derived from the PK model and an energy intake-total daily energy expenditure model.

The method may comprise:

The updated patient data may comprise energy intake data and/or physical activity data. The method comprises updating an energy intake-total energy expenditure model using the updated patient weight data and the energy intake data and/or the physical activity data.

Processing the patient data with a dosage calculator to determine the dosage of the incretin pathway drug for administering to the patient may comprise:

Refining the dose estimate may comprise:

The target drug concentration may comprise a target plasma level. The target plasma level may comprise one or more of:

The dosage calculator may comprise a machine learning algorithm trained using a PK model.

The machine learning algorithm may be trained using a PK model and an energy intake−total daily energy expenditure model.